Numerical analysis of pile group, piled raft, and footing using finite element software PLAXIS 2D and GEO5

Foundation plays a vital role in weight transfer from the superstructure to substructure. However, foundation characteristics such as pile group, piled raft, and footing remain unfolded due to their highly non-linear behaviour in different soil types. Bibliography analysis using VOSvierwer algorithm supported the significance of the research. Hence, this study investigates the load-bearing capacity of different types of foundations, including footings, pile groups, and piled rafts, by analyzing experimental data using finite element tools such as PLAXIS 2D and GEO5. The analysis involves examining the impact of various factors such as the influence of surcharge and the effect of different soil types on the load-bearing capabilities of the different types of foundation. For footing, parametric investigations using PLAXIS 2D are conducted to explore deformational changes. Pile groups are analyzed using GEO5 to assess their factor of safety (FOS.) and settling under various criteria, such as pile length and soil type. The study also provides insight into selecting the right type of foundation for civil engineering practice. Findings showed that different soil types have varying deformational behaviours under high loads with sandy soil having less horizontal deformation than clayey soil. Also, it was observed that increasing the pile thickness by 50% resulted in a reduction of 13.88% in settlement and an improvement of 16.66% in the FOS. In conclusion, this study highlights the importance of professionalism, exceptional talent, and outstanding decision-making when assessing the load-bearing capabilities of various foundation types for building structures. © 2023, Springer Nature Limited.Ambo University, AUThe authors are thankful to the Department of Civil Engineering, Ambo University for providing the necessary material and resources to achieve the set objectives. Also, the authors would like to acknowledge the anonymous reviewers who comments have improved this manuscript

Prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model

Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay. © 2021 Elsevier Lt

Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models

This study investigates the performance of support vector machine (SVM), multivariate adaptive regression spline (MARS), and random forest (RF) models for predicting the lead (Pb) adsorption by attapulgite clay. Models are constructed using batch stochastic data of heavy metal (HM) concentrations under different physicochemical conditions. Implementation of auto-hyper-parameter tuning using grid-search approach and comparative analysis is performed against the benchmark artificial intelligence (AI) models. Models are constructed based on Pb concentration (IC), the dosage of attapulgite clay (dose), contact time (CT), pH, and NaNO3 (SN). Principle component analysis (PCA) and correlation analysis (CA) methods are integrated to assess the importance of the applied predictors and their relationship with the target. Research findings approved the potential of the grid-RF model as a marginal superior predictive model against the grid-SVM in terms of MAE, i.e., 3.29 and 3.34, respectively; moreover, the md scored the same, i.e., 0.93, which reveals the potential predictability for both. Nonetheless, grid-MARS and standalone MARS models remained likewise in their predictability. IC parameter demonstrated the highest influential among all the predictors with the highest value of importance in the case of all three evaluators. The solution pH and dose stands together with marginal differences in case of PCA method; however, solution pH and CT appeared with similarity impact using the PCA method. Graphical abstract: [Figure not available: see fulltext.]. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature

prediction of copper ions adsorption by attapulgite adsorbent using tuned-artificial intelligence model

Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay

Artificial intelligence models for suspended river sediment prediction : state-of-the art, modeling framework appraisal, and proposed future research directions

202303 bcwwVersion of RecordSelf-fundedPublishe

Alpha-Fetoprotein- and CD40Ligand-Expressing Dendritic Cells for Immunotherapy of Hepatocellular Carcinoma

Dendritic cells (DC) as professional antigen presenting cells are able to prime T-cells against the tumor-associated antigen α-fetoprotein (AFP) for immunotherapy of hepatocellular carcinoma (HCC). However, a strong immunosuppressive tumor environment limits their efficacy in patients. The co-stimulation with CD40Ligand (CD40L) is critical in the maturation of DC and T-cell priming. In this study, the impact of intratumoral (i.t.) CD40L-expressing DC to improve vaccination with murine (m)AFP-transduced DC (Ad-mAFP-DC) was analyzed in subcutaneous (s.c.) and orthotopic murine HCC. Murine DC were adenovirally transduced with Ad-mAFP or Ad-CD40L. Hepa129-mAFP-cells were injected into the right flank or the liver of C3H-mice to induce subcutaneous (s.c.) and orthotopic HCC. For treatments, 106 Ad-mAFP-transduced DC were inoculated s.c. followed by 106 CD40L-expressing DC injected intratumorally (i.t.). S.c. inoculation with Ad-mAFP-transduced DC, as vaccine, induced a delay of tumor-growth of AFP-positive HCC compared to controls. When s.c.-inoculation of Ad-mAFP-DC was combined with i.t.-application of Ad-CD40L-DC synergistic antitumoral effects were observed and complete remissions and long-term survival in 62% of tumor-bearing animals were achieved. Analysis of the tumor environment at different time points revealed that s.c.-vaccination with Ad-mAFP-DC seems to stimulate tumor-specific effector cells, allowing an earlier recruitment of effector T-cells and a Th1 shift within the tumors. After i.t. co-stimulation with Ad-CD40L-DC, production of Th1-cytokines was strongly increased and accompanied by a robust tumor infiltration of mature DC, activated CD4+-, CD8+-T-cells as well as reduction of regulatory T-cells. Moreover, Ad-CD40L-DC induced tumor cell apoptosis. Intratumoral co-stimulation with CD40L-expressing DC significantly improves vaccination with Ad-mAFP-DC in pre-established HCC in vivo. Combined therapy caused an early and strong Th1-shift in the tumor environment as well as higher tumor apoptosis, leading to synergistic tumor regression of HCC. Thus, CD40L co-stimulation represents a promising tool for improving DC-based immunotherapy of HCC